Mechanical ventilators assisting spontaneously breathing patients strive to synchronize their performance with the patient's efforts. To do this, ventilators typically measure one or more of pressure, volume, flow and time and compare that measure with predetermined thresholds. Some ventilators use respiratory bands around the chest and abdomen of the patient to detect patient effort. The ventilator may then adjust the pressure, volume or flow of air being delivered to the patient in accordance with measure of the patient's efforts. For example, a flow-triggered pressure controlled device may deliver air at one fixed pressure to a patient until the flow crosses a threshold level, whereupon the pressure is changed to another fixed pressure. Depending on their conditions, different patients may experience different levels of discomfort depending upon how quickly and accurately the ventilator tracks the patients' efforts. Simple threshold tests may fail when breaths are irregular, for example, during the presence of coughs, sighs and snores. An improved method and apparatus for ventilator patient synchronization is described in Patent Cooperation Treaty Application PCT/AU97/00631 with publication number WO 98/12965 (Berthon-Jones) where the phase of the patient's respiratory cycle is determined from flow data using fuzzy logic. The specification is hereby included by cross-reference.
Typical apparatus includes a servo 3 controlled blower, comprised of a motor 2 and an impeller 1 connected to a patient interface 5 via an air delivery conduit 6, as shown in FIG. 1. The controller 4 is typically a computer, a processor including memory, or a programmable circuit. One example of patient interface is a nasal mask, others include nose and mouth masks, full face masks and nasal pillows. The pressure in the mask may be measured by a transducer 11 having direct contact with the mask, or alternatively, the transducer may be physically situated in the blower main housing and may estimate the mask pressure using correlations. Flow transducers 10 or other means for measuring flow may also be situated in the mask or in the blower main housing. There are various displays 8 and switches 7 on the blower housing. There is an interface 15 to enable the apparatus to communicate with other devices. Some apparatus include a fixed speed blower whose output is controllably variably vented to atmosphere providing a controlled variable pressure to the patient.
“Expert” systems are known to be used for assisting with medical diagnosis. Such expert systems are typically said to comprise two parts, a “knowledge base” and an “inferencing engine.” The knowledge base comprises the set of “expert” information about the system which is used to guide interpretation of the data which has been observed. Sophisticated expert systems may include hundreds, or thousands of pieces of information in the knowledge base. The fuzzy membership rules and weights of Berthon-Jones may be interpreted as the knowledge base. The inferencing engine is the mechanism which combines the knowledge base with the experimental evidence to reach the conclusion. Several different inferencing engines are known, such as those based on fuzzy logic, rule based reasoning and Bayesian likelihoods.
Bayes' theorem1 quantifies the intuitively appealing proposition that prior knowledge should influence interpretation of experimental observations. One form of Bayes' theorem is: 1Armitage & Berry (1994) Statistical Methods in Medical Research, 3rd Edition, p 72, Blackwell Science Ltd, Oxford, United Kingdom ISBN 0-632-03695-8
                              L          ⁡                      (                                          H                i                            |                              F                j                                      )                          =                                            L              ⁡                              (                                  H                  i                                )                                      ⁢                          L              ⁡                              (                                                      F                    j                                    |                                      H                    i                                                  )                                                                        ∑              n                        ⁢                                          L                ⁡                                  (                                      H                    n                                    )                                            ⁢                              L                ⁡                                  (                                                            F                      j                                        |                                          H                      n                                                        )                                                                                        (        1        )            where L is a likelihood or probability function. Thus, L(H|F) is the likelihood of an hypothesis being true, given observation F, L(H) is the likelihood of the hypothesis being true, and L(F|H) is the likelihood of the observation given the hypothesis being true.
For example, if a physician has observed a particular symptom in a patient, in deciding whether the patient has a particular disease, the physician draws upon the prior evidence of the likelihood that the patient has the particular disease. Several independent observations may be used in conjunction with prior likelihoods to determine the likelihood that an hypothesis is true. The decision may be taken to be the most likely hypothesis.